Hostname: page-component-848d4c4894-x5gtn Total loading time: 0 Render date: 2024-06-11T07:52:47.816Z Has data issue: false hasContentIssue false
  • English
  • Français

The evolution of a mobile payment solution network

Published online by Cambridge University Press:  17 April 2019

Kjersti Aas  and
Hanne Rognebakke
Kjersti Aas*
Affiliation:
Norwegian Computing Center, P. O. Box 114, Blindern, N-0314 Oslo, Norway
Hanne Rognebakke
Affiliation:
Norwegian Computing Center, P. O. Box 114, Blindern, N-0314 Oslo, Norway
*
*Corresponding author. Emails: Kjersti.Aas@nr.no, Hanne.Rognebakke@nr.no
Get access Rights & Permissions [Opens in a new window]

Abstract

Vipps is a peer-to-peer mobile payment solution launched by Norway’s largest financial services group DNB. The Vipps transaction data may be viewed as a graph with users corresponding to the nodes, and the financial transactions between the users defining the edges. We have followed the evolution of this graph from May 2015 to September 2016. This is a unique data set, as information about transactions of individuals is usually not available for research. In this paper, we use an advanced statistical model where preferential attachment is combined with fitness. We show that the intrinsic quality of the nodes in the Vipps network plays an important part in the evolution of the network. This insight may, e.g., be used to identify influential nodes for viral marketing.

Keywords

mobile payment solutionnetwork evolutionpreferential attachmentfitness
Type
Original Article
Information
Network Science , Volume 7 , Issue 3 , September 2019 , pp. 422 - 437
Copyright
© Cambridge University Press 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Barabasi, A.-L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286, 509–512.CrossRefGoogle ScholarPubMed
Barabasi, A.-L., Albert, R., & Jeong, H. (2000). Scale-free characteristics of random networks: The topology of the world-wide web. Physica A: Statistical Mechanics and Its Applications, 281, 69–77.CrossRefGoogle Scholar
Bianconi, G., & Barabˡsi, A. L. (2001). Competition and multiscaling in evolving networks. Europhysics Letters, 54, 436–442.CrossRefGoogle Scholar
Borgs, C., Chayes, J., Daskalakis, C., & Roch, S. (2007). First to market is not everything: An analysis of preferential attachment with fitness. In Proceedings of the thirty-ninth annual acm symposium on theory of computing. STOC ‘07 (pp. 135–144). New York, NY, USA: ACM.Google Scholar
Caldarelli, G., Capocci, A., De Los Rios, P., & Muñoz, M. A. (2002). Scale-free networks from varying vertex intrinsic fitness. Physical Review Letters, 89, 258702-1–258702-4.CrossRefGoogle ScholarPubMed
Callaway, D. S., Hopcroft, J. E., Kleinberg, J. M., Newman, M. E. J., & Strogatz, S. H. (2001). Are randomly grown graphs really random? Physical Review E, 64, 041902.CrossRefGoogle ScholarPubMed
Cole, S. R., Chu, H., & Greenland, S. (2014). Maximum likelihood, profile likelihood, and penalized likelihood: A primer. American Journal of Epidemiology, 179, 252–260.CrossRefGoogle ScholarPubMed
Dereich, S., & Mörters, P. (2009). Random networks with sublinear preferential attachment: degree evolutions. Electronic Journal of Probability, 14, 1222–1267.CrossRefGoogle Scholar
Hunter, D., & Lange, K. (2000). Quantile regression via an MM algorithm. Journal of Computational Statistics and Data Analysis, 9, 60–77.Google Scholar
Iñiguez, G., Ruan, Z., Kaski, K., Kertész, J., & Karsai, M. (2017). Service adoption spreading in online social networks. arXiv preprint, arXiv:1706.09777.Google Scholar
Kondor, D., Posfai, M., Csabai, I., & Vattay, G. (2014). Do the rich get richer? An empirical analysis of the bitcoin transaction network. PLOS ONE, 9, e86197.CrossRefGoogle ScholarPubMed
Kong, J. S., Sarshar, N., & Roychowdhury, V. P. (2008). Experience versus talent shapes the structure of the web. Proceedings of the National Academy of Sciences, 105(37), 13724–13729.CrossRefGoogle ScholarPubMed
Krapivsky, P. L., Redner, S., & Leyvraz, F. (2000). Connectivity of growing random networks. Physics Review Letters, 85, 4629–4632.CrossRefGoogle ScholarPubMed
Krapivsky, P. L., Rodgers, G. J., & Redner, S. (2001). Organization of growing networks. Physical Review E, 63, 066123-1–066123-14.CrossRefGoogle Scholar
Kunegis, J., Blattner, M., & Moser, C. (2013). Preferential Attachment in Online Networks: Measurement and Explanations. Presented at WebSci’13 Conference, Paris.CrossRefGoogle Scholar
Leskovec, J., Singh, A., & Kleinberg, J. (2006). Patterns of influence in a recommendation network. In Proceedings of the 10th pacific-asia conference on advances in knowledge discovery and data mining. PAKDD’06 (pp. 380–389). Berlin, Heidelberg: Springer-Verlag.Google Scholar
Pham, T., Sheridan, P., & Shimodaira, H. (2015). PAFit: A statistical method for measuring preferential attachment in temporal complex networks. PLOS ONE, 9 , e0137796.CrossRefGoogle Scholar
Pham, T., Sheridan, P., & Shimodaira, H. (2016). Joint estimation of preferential attachment and node fitness in the evolution of complex networks. Nature Scientific Reports, 6, 1–13.Google ScholarPubMed
Pham, T., Sheridan, P., & Shimodaira, H. (2017). PAFit: An R Package for Estimating Preferential Attachment and Node Fitness in Temporal Complex Networks. arXiv preprint, arXiv:1704.06017.Google Scholar
Redner, S. (1998). How popular is your paper? an empirical study of the citation distribution. The European Physical Journal B - Condensed Matter and Complex Systems, 4, 131–134.CrossRefGoogle Scholar
Stonedahl, F., Rand, W., & Wilensky, U. (2010). Evolving Viral Marketing Strategies. In Proceedings of the 12th annual conference on Genetic and evolutionary computation.CrossRefGoogle Scholar
Yule, G. U. (1925). A mathematical theory of evolution, based on the conclusions of Dr. J. C. Willis, F.R.S. Philosophical Transactions of the Royal Society B, 213, 21–87.CrossRefGoogle Scholar